真实有雾场景下的目标检测

Accurate object detection in the real-world hazy scene is very important to some potential visual task, such as video surveillance, smart city, autonomous driving and so on. This paper focuses on two research problems, which are to build a synthetic dataset of object detection in hazy scene and to analyze the effect of prior knowledge and joint learning of model on object detection in real-world hazy scene. Two frameworks are proposed which are the knowledge-guided object detection (KODNet) and the joint learning in dehazing and object detection (DONet). In KODNet, statistical prior knowledge will be used to guide the general object detection network to learn object features in the hazy scene during the training, makes the general object detector better adapt to the special object detection scenario. DONet can effectively solve the problem of structural detail missing and color distortion caused by image dehazing, thereby realizing the improvement of the objects detection accuracy in real-world scene. The experimental results on RTTS show that KODNet and DONet are effective to the object detection in the real-world hazy scene and they achieve the mAP of 70.5% and 66.6%.